PyData: PyData & Scientific Libraries Stack Session List
(Serious) Time for Time Series
Marysia Winkels, James Hayward
Time SeriesFrom inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. So join us and learn about time series analysis and seasonality modelling.
5 Steps to Speed Up Your Data-Analysis on a Single Core
Jonathan Striebel
Data Engineering, PerformanceYour data analysis pipeline works. Nice. Could it be faster? Probably. Do you need to parallelize? Not yet. Discover optimization steps that boost the performance of your data analysis pipeline on a single core, reducing time & costs.
Creating 3D Maps using Python
Martin Christen
GIS / Geo-AnalyticsCreate 3DMaps anywhere on the planet using Python and OpenData
Data Apis: Standardization of N-dimensional arrays and dataframes
Stephannie Jimenez Gacha
APIsIntroduction to the consortium of Data APIs, where we will be presenting our motivation, objectives and progress of the standardization process after one year of activity.
Data Science at Scale with Dask
Richard Pelgrim
APIs, Big Data, CloudA hands-on introduction to methods for scaling your data science and machine learning with Dask.
Easy and flexible imaging with the Core Imaging Library
Vaggelis Papoutsellis, Dr. Jakob Sauer Jørgensen
Algorithms, Big Data, MathCore Imaging Library is an open-source, object-oriented Python library for inverse problems in imaging developed by the UK academic network CCPi.
Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, ReproducibilityFlexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Introducing the Dask Active Memory Manager
Guido Imperiale
Algorithms, Architecture, Backend, Cloud, Data Engineering, Distributed Computing, Parallel Programming / AsyncThe Active Memory Manager is a new experimental feature of Dask which aims to reduce the memory footprint of the cluster, prevent hard to debug out-of-memory issues, and make worker retirement more robust.
On Blocks, Copies and Views: updating pandas' internals
Joris Van den Bossche
APIs, Data StructuresAs a pandas user, did you ever run into the SettingWithCopyWarning? Quite likely, and this is one of the more confusing aspects of pandas. But it doesn’t have to be this way! Check my proposal to simplify this aspect of pandas
Reproducible machine learning and science with python
Prabhant Singh
Best Practice, Community, ScienceLearn how to create reproducible workflows, benchmarks and studies with openml-python
sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz Kiraly
Algorithms, Predictive Modelling, Time SeriesThe forecasting module of sktime provides a unified, sklearn-compatible, and composable interface. This tutorial covers advanced topics in forecasting using sktime: probabilistic forecasting, and forecasting with panel data, including global/hierarchical forecasting.
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